Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Voting Rules As Error-Correcting Codes
Authors: Ariel Procaccia, Nisarg Shah, Yair Zick
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results from real data show that our approach produces significantly more accurate rankings than alternative approaches. |
| Researcher Affiliation | Academia | Ariel D. Procaccia Carnegie Mellon University EMAIL Nisarg Shah Carnegie Mellon University EMAIL Yair Zick Carnegie Mellon University EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing open-source code or a link to a code repository for their methodology. |
| Open Datasets | Yes | Mao, Procaccia, and Chen (2013) collected these datasets dots and puzzle via Amazon Mechanical Turk. |
| Dataset Splits | No | The paper describes the datasets and their use in evaluation but does not specify training, validation, or test splits in terms of percentages or counts for model training or tuning. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We use the average error in a profile as the bound t given to OPTd, i.e., we compute OPTd(t , π) on profile π where t = d(π, σ ). [...] To synchronize the results across different profiles, we use r = (bt MAD)/(t MAD), where MAD is the minimum average distance of any ranking from the votes in a profile, that is, the average distance of the ranking returned by MINISUMd from the input votes. For all profiles, r = 0 implies bt = MAD (the smallest value that admits a possible ground truth) and r = 1 implies bt = t (the true average error). In our experiments we use r [0, 2]; here, bt is an overestimate of t for r (1, 2] (a valid upper bound on t ), but an underestimate of t for r [0, 1) (an invalid upper bound on t ). |